Microbial ecosystems consist of many interacting components that integrate through stochastic and highly dynamic processes across multiple scales. Yet, despite this complexity, microbial communities exhibit remarkably robust patterns and reproducible functions. This apparent paradox reflects the role of constraints, whether physical, physiological, or evolutionary, that channel stochasticity into structured outcomes. Due to the limited knowledge of the nature of these constraints, models in ecology have traditionally relied on stochastic exploration under minimal mechanistic assumptions. Now, advances in data availability and computational methods increasingly allow us to construct models that incorporate explicit mechanistic constraints. In this review, we synthesize emerging modeling approaches that explore the space of ecological possibility in microbial ecosystems under realistic constraints, such as those imposed by metabolic stoichiometry, thermodynamics, or the structure of ecological interaction networks. We argue that integrating such constraints can significantly improve the predictive resolution of models, helping us build a much needed bridge between theory and data. We further discuss how novel statistical approaches are revealing simple, low-dimensional patterns in microbial communities, offering empirical clues for identifying the underlying constraints. Together, these developments suggest a path toward a data-driven and mechanistically informed theory in microbial ecology.